This project consists of a high-performance server and client implementation in C++, along with extensive benchmarking using Python. The benchmarks generate various performance graphs, including box plots and pie charts.
├── benchmark
│ ├── bench_box_plot.py # Script to generate box plot from benchmark data
│ ├── benchmark_fast.py # Fast execution benchmark
│ └── benchmark_slow.py # Slow execution benchmark
├── benchmark_fast_results.png # Image of fast benchmark results
├── benchmark_slow_results.png # Image of slow benchmark results
├── docker-compose.yml # Docker Compose configuration
├── Dockerfile # Dockerfile to containerize the project
├── extensive_benchmark_results.png # Image of extensive benchmark results
├── Makefile # Makefile for building the project
├── README.md # This README file
├── requirements.txt # Python dependencies
├── response_time_boxplot.png # Image of response time distribution (Box plot)
├── response_time_piechart.png # Image of response time distribution (Pie chart)
├── src
│ ├── client.cc # Client implementation
│ ├── command.cc, command.h # Command processing logic
│ ├── conn.h # Connection handling
│ ├── epoll_manager.cc, epoll_manager.h # Epoll-based event management
│ ├── server.cc, server.h # Server implementation
│ └── utils.h # Utility functions
└── test
└── test.py # Python test script for validation
docker build -t my_server .docker run -p 6969:6969 --name server_container my_serverdocker run --network host my_server ./clientpython -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`pip install -r requirements.txtpython benchmark/benchmark_fast.py
python benchmark/benchmark_slow.py
python benchmark/bench_box_plot.pyTotal Requests: 1000
Successful Requests: 1000
Total Time: 0.028688 sec
Average Time per Request: 0.000029 sec
Total Requests: 5000
Successful Requests: 5000
Total Time: 1.049078 sec
Average Time per Request: 0.000210 sec
Median Time per Request: 0.000118 sec
Max Time per Request: 0.010576 sec
Min Time per Request: 0.000015 sec
Below are the benchmark images generated:
- Fast Benchmark:
- Slow Benchmark:
- Response Time Box Plot:
python test/test.pymake./server./clientmake clean- Ensure that the server is running before executing the client or benchmark scripts.
- The benchmarks will generate performance graphs to analyze the efficiency of the server.
- Docker is recommended for an isolated and reproducible environment.
🚀 Happy Coding!